The present invention relates to pattern recognition systems. Particularly, the invention relates to systems for analyzing vector data to identify and/or localize objects that are represented by the vector data.
While the invention is applicable to many different fields, it will be described particularly with regard to cell detection and classification. However, the invention is also applicable to many other research fields wherein vectors can be used to describe or otherwise characterize a plurality of objects and the objects can represent other entities than cells.
Considering cell detection, however, cell detection in bright field microscopy is an inherently difficult task due to the immense variability of cell appearance. Even more difficult is the recognition of the subtle differences in appearance that distinguish unstained viable from non-viable cells in bright field images. Although an experienced observer can sometimes recognize these differences, viability stains are commonly used for reliable determination of viability. The requirement of a human observer represents a severe impediment to the development of high throughput systems that require recognition of viable cells. Therefore, there is a great need for effective algorithms that automatically recognize viable cells.
Currently, a typical approach for cell detection is to use fluorescent probes that have a chemical specificity for cell organelles. However, this approach can consume one or more of a very limited number of available fluorescence channels just for the purpose of cell identification. Currently for commercially available microscopes there are typically only four channels for simultaneous monitoring and eight channels for sequential observation, while there are many cellular characteristics for which the fluorescence channels can be used to detect. It is therefore highly desirable to identify cells with a method that uses transmitted light illumination, thereby permitting all available fluorescence channels to be used to obtain cellular and subcellular information for further cell analysis.
Classical image analysis approaches require end-users to have programming skills and require independent optimizations for different cell types. An alternative is to use machine-learning techniques, which avoid end-user programming since classifiers only need to be trained. For example, Artificial Neural Networks (ANNs) have been successfully used to identify cells in bright field images. These algorithms are able to capture complex, nonlinear, relationships in high dimensional feature spaces. However, ANNs are based on the Empirical Risk Minimization (ERM) principle. Therefore, they are prone to false optimizations due to local minima in the optimization function and are susceptible to training problems such as “overfitting.” This makes ANN-training a complex procedure that can be daunting for biologists and others who are not immersed in the complexities of ANNs.
In recent years, Support Vector Machines (SVMs) have been found to be remarkably effective in many real-world applications.
Unlike ANNs, SVMs follow the Structural Risk Minimization (SRM) principle, which aims at minimizing an upper bound of the generalization error. As a result, an SVM tends to perform well when applied to data outside the training set. SVMs also have many desirable properties such as flexibility in choice of kernel function and implicit mapping into high dimensional feature spaces. But what makes SVMs most attractive is that they avoid several major problems associated with ANNs. For example, SVMs control overfitting by restricting the capacity of the classifier. They also depend on the solution of a convex Quadratic Programming (QP) problem which has no local extrema. The unique optimal solution can therefore be efficiently obtained.